oclaw/openclaw/extensions/open-prose/skills/prose/examples/43-rlm-pairwise.prose

# RLM: Pairwise Analysis
# O(n²) tasks through batched pair processing
# Base LLMs: <1% accuracy. RLMs: 58%. (OOLONG-Pairs benchmark)

input items: "Items to compare pairwise"
input relation: "Relationship to identify"

agent comparator:
  model: sonnet
  prompt: "Analyze relationship. Return: {pair, relation, strength, evidence}."

agent mapper:
  model: opus
  prompt: "Build relationship map. Identify clusters and anomalies."

block pairs(list):
  let result = []
  for i, a in list:
    for j, b in list:
      if j > i:
        result = result + [{first: a, second: b}]
  output result

block analyze(items, rel, depth):
  let all_pairs = do pairs(items)

  if **fewer than 100 pairs** or depth <= 0:
    output all_pairs | pmap:
      session: comparator
        prompt: "Analyze {rel}"
        context: item

  let batches = session "Split into batches of ~25 pairs"
    context: all_pairs

  let results = []
  parallel for batch in batches:
    let batch_results = batch | pmap:
      session: comparator
        prompt: "Analyze {rel}"
        context: item
    results = results + batch_results

  output results

let relationships = do analyze(items, relation, 2)

output map = session: mapper
  prompt: "Build {relation} map"
  context: { items, relationships }